@inproceedings{wang-etal-2026-opera,
title = "{OP}e{RA}: A Dataset of Observation, Persona, Rationale, and Action for Evaluating {LLM}s on Human Online Shopping Behavior Simulation",
author = "Wang, Ziyi and
Lu, Yuxuan and
Li, Wenbo and
Amini, Amirali and
Sun, Bo and
Bart, Yakov and
Lyu, Weimin and
Gesi, Jiri and
Wang, Tian and
Huang, Jing and
Su, Yu and
Ehsan, Upol and
Alikhani, Malihe and
Li, Toby Jia-Jun and
Chilton, Lydia and
Wang, Dakuo",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.2033/",
pages = "43942--43960",
ISBN = "979-8-89176-390-6",
abstract = "Can Large Language models (LLMs) accurately simulate the next web action of a specific user? While LLMs have shown promising capabilities in generating believable human behaviors, evaluating their ability to mimic real user behaviors remains an open challenge, largely due to the lack of high-quality, publicly available datasets that capture both the observable actions and the internal reasoning of an actual human user. To address this gap, we introduce OPeRA, a novel dataset of Observation, Persona, Rationale, and Action collected from real human participants during online shopping sessions. **OPeRA is the first public dataset that comprehensively captures: user personas, browser observations, fine-grained web actions, and self-reported just-in-time rationales**. We developed both an online questionnaire and a custom browser plugin to gather this dataset with high fidelity. Using OPeRA, we establish **the first benchmark to evaluate how well current LLMs can predict a specific user{'}s next action** and rationale with a given persona and {\ensuremath{<}}observation, action, rationale{\ensuremath{>}} history. This dataset lays the groundwork for future research into LLM agents that aim to act as personalized digital twins for human."
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<abstract>Can Large Language models (LLMs) accurately simulate the next web action of a specific user? While LLMs have shown promising capabilities in generating believable human behaviors, evaluating their ability to mimic real user behaviors remains an open challenge, largely due to the lack of high-quality, publicly available datasets that capture both the observable actions and the internal reasoning of an actual human user. To address this gap, we introduce OPeRA, a novel dataset of Observation, Persona, Rationale, and Action collected from real human participants during online shopping sessions. **OPeRA is the first public dataset that comprehensively captures: user personas, browser observations, fine-grained web actions, and self-reported just-in-time rationales**. We developed both an online questionnaire and a custom browser plugin to gather this dataset with high fidelity. Using OPeRA, we establish **the first benchmark to evaluate how well current LLMs can predict a specific user’s next action** and rationale with a given persona and \ensuremath<observation, action, rationale\ensuremath> history. This dataset lays the groundwork for future research into LLM agents that aim to act as personalized digital twins for human.</abstract>
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%0 Conference Proceedings
%T OPeRA: A Dataset of Observation, Persona, Rationale, and Action for Evaluating LLMs on Human Online Shopping Behavior Simulation
%A Wang, Ziyi
%A Lu, Yuxuan
%A Li, Wenbo
%A Amini, Amirali
%A Sun, Bo
%A Bart, Yakov
%A Lyu, Weimin
%A Gesi, Jiri
%A Wang, Tian
%A Huang, Jing
%A Su, Yu
%A Ehsan, Upol
%A Alikhani, Malihe
%A Li, Toby Jia-Jun
%A Chilton, Lydia
%A Wang, Dakuo
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F wang-etal-2026-opera
%X Can Large Language models (LLMs) accurately simulate the next web action of a specific user? While LLMs have shown promising capabilities in generating believable human behaviors, evaluating their ability to mimic real user behaviors remains an open challenge, largely due to the lack of high-quality, publicly available datasets that capture both the observable actions and the internal reasoning of an actual human user. To address this gap, we introduce OPeRA, a novel dataset of Observation, Persona, Rationale, and Action collected from real human participants during online shopping sessions. **OPeRA is the first public dataset that comprehensively captures: user personas, browser observations, fine-grained web actions, and self-reported just-in-time rationales**. We developed both an online questionnaire and a custom browser plugin to gather this dataset with high fidelity. Using OPeRA, we establish **the first benchmark to evaluate how well current LLMs can predict a specific user’s next action** and rationale with a given persona and \ensuremath<observation, action, rationale\ensuremath> history. This dataset lays the groundwork for future research into LLM agents that aim to act as personalized digital twins for human.
%U https://aclanthology.org/2026.acl-long.2033/
%P 43942-43960
Markdown (Informal)
[OPeRA: A Dataset of Observation, Persona, Rationale, and Action for Evaluating LLMs on Human Online Shopping Behavior Simulation](https://aclanthology.org/2026.acl-long.2033/) (Wang et al., ACL 2026)
ACL
- Ziyi Wang, Yuxuan Lu, Wenbo Li, Amirali Amini, Bo Sun, Yakov Bart, Weimin Lyu, Jiri Gesi, Tian Wang, Jing Huang, Yu Su, Upol Ehsan, Malihe Alikhani, Toby Jia-Jun Li, Lydia Chilton, and Dakuo Wang. 2026. OPeRA: A Dataset of Observation, Persona, Rationale, and Action for Evaluating LLMs on Human Online Shopping Behavior Simulation. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 43942–43960, San Diego, California, United States. Association for Computational Linguistics.